journal article·2004-12-01

Application of a Web-enabled real-time structural health monitoring system for civil infrastructure systems

S F Masri, L-H Sheng, J P Caffrey, R L Nigbor, M Wahbeh, A M Abdel-Ghaffar

Smart Materials and Structures· Volume 13· Issue 6
Publisher page

Abstract

The system architecture of a novel structural health monitoring system that is optimized for the continuous real-time monitoring of dispersed civil infrastructures is presented. The monitoring system is based on a highly efficient multithreaded software design that allows the system to acquire data from a large number of channels, monitor and condition this data, and distribute it, in real time, over the Internet to multiple remote locations. Bandwidth and latency issues that impact the operation of monitoring systems are discussed. The application of the monitoring system under discussion to a long span, flexible bridge in the metropolitan Los Angeles region is described. The bridge had previously been instrumented with 26 strong motion accelerometers. Sample 'quick analysis' results continuously provided by the monitoring system are presented and interpreted. System identification results, obtained through off-line batch processing, are presented for a data set from a recent earthquake that automatically triggered the recording capability of the system. It is shown that, using a time domain system identification approach, the bridge stiffness and damping matrices can be identified from the earthquake data set and subsequently used to determine the bridge modal properties, such as frequencies and damping ratios. In this approach the bridge is modeled as a multi-input/multi-output system with order compatible with the number of available sensors. Implementation issues requiring further investigation are presented and discussed.
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Research context

OpenAlex topics: Structural Health Monitoring Techniques · Seismic Performance and Analysis · Geophysics and Sensor Technology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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